Online tuned neural networks for PV plant production forecasting

Abstract

The paper deals with the forecast of the power production for three different PhotoVoltaic (PV) plants using an on-line self learning prediction algorithm. The plants are located in Italy at different latitudes. This learning algorithm is based on a radial basis function (RBF) network and combines the growing criterion and the pruning strategy of the minimal resource allocating network technique. Its on-line learning mechanism gives the chance to avoid the initial training of the NN with a large data set. The performances of the algorithm are tested on the three PV plants with different peak power, panel's materials, orientation and tilting angle. Results are compared to a classical RBF neural network.